Building a web application for Medical Entity Detection using AWS Comprehend

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Named Entity Recognition (NER) is one of the most popular and in-demand NLP tasks. As NER has expanded it has become more domain specific as well. Building custom NER models for a specific domain such as healthcare/medical, can be difficult and require extensive amounts of data and computing power. AWS Comprehend is a high-level service, AWS offers that automates many different NLP tasks such as Sentiment Analysis, Topic Modeling, and NER. Comprehend branched out to create an sub-service called Comprehend Medical, that is specifically geared for Medical NER. In this article we will cover how to build a web application…

Tips for your first full-time job after graduating

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It’s almost been three months since I’ve started working full-time after graduating this past December. It’s truly been a whirlwind of confusion, excitement, frustration, doubts, and nostalgia that has swirled in my head as I’ve settled down into the dreaded, monotone 9–5 life. To be fully honest, at first it was a lot of fear, anxiety, and whole lot of doubts over whether I’d made the right choice of pursuing a job right now. It certainly didn’t help that we’re currently in a global pandemic and forced to work from home, while my apartment-mates shotgun their way through what should…

Comes with free Data Science notes/code on various ML algorithms documented in Notion

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With Data Science being such an interdisciplinary field that has new tools almost every month, it is becoming more and more important to document your work and learning. Notion provides a beautiful workspace that is an engineer’s dream. You can map out/visualize project schedules, create to-do lists, build small websites with documentation, and build custom workspaces for whatever specific topic/project you are working on. As an active learner, yet unorganized individual I needed a source that was able to document small details and features that I could easily access later for future projects or problems I ran into. Notion is…

An end to end code demonstration and explanation of SageMaker’s in-built algorithm Linear Learner.

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AWS SageMaker is booming and proving to be one of the top services for building ML models and pipelines on the cloud. One of the best features of SageMaker is the wide array of in-built algorithms that it provides for Data Scientists and Developers to quickly train and deploy their models. For those less experienced with model creation in a certain field, these algorithms do all the work behind the scenes while all you have to do is feed your data for training. To check out all the algorithms and uses cases SageMaker provides click here. …

Code demonstration of building a web application that accesses AWS API Gateway, Lambda, and Comprehend to visualize Sentiment Analysis

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AWS has a variety of services in ML/AI, Robotics, and IoT that can bring life to whatever application you’re trying to build. To be able to access these services, however you need to be able to stitch together a basic full-stack template integrating AWS API Gateway and Lambda with whatever front-end you’re working with. For this demonstration, we’ll be building a Sentiment Analysis web application that accesses a REST API created on AWS API Gateway which then hits an AWS Lambda function that accesses AWS Comprehend. We’ll then process the Comprehend response to visualize the data using PlotlyJS. The purpose…

Code demonstration on building, training, and deploying custom TF 2.0 models using Sagemaker’s TensorFlow Estimator

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Building deep learning models and pipelines locally can prove to be very computationally expensive. This has led to a rise in popularity for cloud computing providers such as AWS, Microsoft Azure, and more. SageMaker is Amazon’s main Machine Learning service that enables developers to build, train, and deploy models at scale. SageMaker offers a Jupyter Notebook like environment that allows for developers to build custom models with frameworks such as Tensorflow, PyTorch, and MXNet. Training the model however is not as simple as running a cell in a traditional Jupyter Notebook. I wanted to walk through an end to end…

An introduction to Brain.js and Code Demonstrations of its Neural Network features.

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Python has been the primary language for most Deep/Machine Learning enthusiasts, but there are quite a few JavaScript libraries that bring the magic of ML directly to the browser. Brain.js is one of the most popular JavaScript ML libraries known for its simple and easy usage. The library greatly simplifies building and training Neural Networks to just a few lines of code eliminating much of the math and jargon needed to fully understand the theoretical aspects of the model.

Brain.js supports a few different Neural Network type, for this demonstration I will be looking into a Feedforward (ANN) and LSTM…

Hands-on Tutorials

A code demonstration and explanation of the basics of Streamlit while solving NLP tasks such as Sentiment Analysis, NER, and Text Summarization.

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One of the most common tasks Data Scientists struggle with is presenting their model/project in a format for users to interact with. The model is not of much use to any external users if it is not presentable in some type of application. This introduces the vast-field of web/app development which then leads to more languages and tools such as HTML, CSS, ReactJS, Dash, Flask, and more that can help you create a front-end interface for users to interact with and get results out of your model. …

Quick overview of using AWS Lambda, Boto3, and Comprehend for high-level NLP tasks in Python

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Amazon Web Services (AWS) has been constantly expanding its Machine Learning services in various domains. AWS Comprehend is the AWS powerhouse for Natural Language Processing (NLP). Two common projects in NLP include Sentiment Analysis and Entity Extraction. Often times we build Custom Models from scratch using libraries such as NLTK, Spacy, Transformers, etc. While custom models definitely have their purpose and perform especially well when you have domain knowledge of the problem you are attacking, they also are very time-consuming to build from ground-up. This is where AWS Comprehend comes in, offering high-level services for Sentiment Analysis and other NLP…

Quick refresher of fundamental Machine Learning models with Code Demonstrations for review.

With the number of Machine Learning algorithms constantly growing it is nice to have a reference point to brush up on some of the fundamental models, be it for an interview or just a quick refresher. I wanted to provide a resource of some of the most common models pros and cons and sample code implementations of each of these algorithms in Python.

Table of Contents

  1. Multiple Linear Regression
  2. Logistic Regression
  3. k-Nearest Neighbors (KNN)
  4. k-Means Clustering
  5. Decision Trees/Random Forest
  6. Support Vector Machine (SVM)
  7. Naive Bayes

1. Multiple Linear Regression


  • Easy to implement, theory is not complex, low computational power compared to other algorithms.
  • Easy to interpret coefficients

Ram Vegiraju

Incoming Solutions Architect @ Amazon. Passionate about Data Science/ML.

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